_base_ = ["../_base_/default_runtime.py"]

dataset_type = "HLDataset"
data_root = "data/phcl/"
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    #     dict(type='PhotoMetricDistortion'),
    #     dict(
    #         type='Expand',
    #         mean=img_norm_cfg['mean'],
    #         to_rgb=img_norm_cfg['to_rgb'],
    #         ratio_range=(1, 2)),
    dict(
        type="MinIoURandomCrop",
        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
        min_crop_size=0.3,
    ),
    dict(type="Resize", img_scale=[(540, 960), (720, 1280)], keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(720, 1280),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=8,
    train=dict(
        type=dataset_type,
        ann_file=[
            data_root + "annotations/widerface_train.json",
            data_root + "annotations/license-plate.json",
        ],
        img_prefix=[data_root + "widerface_train/", data_root],
        pipeline=train_pipeline,
    ),
    val=dict(
        type=dataset_type,
        ann_file=[
            data_root + "annotations/widerface_val.json",
            data_root + "annotations/license-plate_val.json",
        ],
        img_prefix=[data_root + "widerface_val/", data_root],
        pipeline=test_pipeline,
    ),
    test=dict(
        type=dataset_type,
        ann_file=[
            data_root + "annotations/widerface_val.json",
            data_root + "annotations/license-plate_val.json",
        ],
        img_prefix=[data_root + "widerface_val/", data_root],
        pipeline=test_pipeline,
    ),
)
evaluation = dict(interval=1, metric="mAP")


model = dict(
    type="GFL",
    pretrained="pretrained/resnet18-5c106cde.pth",
    backbone=dict(
        type="ResNet",
        depth=18,
        num_stages=4,
        out_indices=(2,),
        frozen_stages=1,
        norm_cfg=dict(type="SyncBN", requires_grad=True),
        norm_eval=True,
        style="pytorch",
    ),
    neck=dict(
        type="DilateEncoder",
        in_channels=256,
        out_channels=512,
        encoder_channels=512,
        norm_cfg=dict(type="SyncBN", requires_grad=True),
    ),
    bbox_head=dict(
        type="SimpleATSSHead",
        num_classes=2,
        in_channels=512,
        stacked_convs=3,
        feat_channels=128,
        norm_cfg=dict(type="SyncBN", requires_grad=True),
        anchor_generator=dict(
            type="AnchorGenerator",
            ratios=[1],
            scales=[0.25, 0.5, 1, 2, 4, 8],
            strides=[16],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.1, 0.1, 0.2, 0.2],
        ),
        loss_cls=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_bbox=dict(type="GIoULoss", loss_weight=2.0),
        loss_centerness=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
        ),
    ),
)
# training and testing settings
train_cfg = dict(
    assigner=dict(type="ATSSAssigner", topk=15),
    allowed_border=-1,
    pos_weight=-1,
    debug=False,
)
test_cfg = dict(
    nms_pre=1000,
    min_bbox_size=0,
    score_thr=0.05,
    nms=dict(type="nms", iou_thr=0.6),
    max_per_img=100,
)

# optimizer
optimizer = dict(
    type="SGD",
    lr=0.12,
    momentum=0.9,
    weight_decay=0.0001,
    paramwise_cfg=dict(custom_keys={".backbone": dict(lr_mult=0.33)}),
)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.001, step=[18, 22]
)
total_epochs = 24

seed = 166
find_unused_parameters = True
